Towards a Unified Approach to Homography Estimation Using Image Features and Pixel Intensities

TL;DR

This paper introduces a hybrid homography estimation method that unifies feature- and intensity-based approaches into a single nonlinear optimization, improving convergence and robustness in vision tasks.

Contribution

A novel unified nonlinear optimization approach for homography estimation that combines feature and intensity methods into one framework.

Findings

Improved convergence over individual methods

Enhanced robustness to illumination changes

Validated in visual tracking application

Abstract

The homography matrix is a key component in various vision-based robotic tasks. Traditionally, homography estimation algorithms are classified into feature- or intensity-based. The main advantages of the latter are their versatility, accuracy, and robustness to arbitrary illumination changes. On the other hand, they have a smaller domain of convergence than the feature-based solutions. Their combination is hence promising, but existing techniques only apply them sequentially. This paper proposes a new hybrid method that unifies both classes into a single nonlinear optimization procedure, applies the same minimization method, and uses the same homography parametrization and warping function. Experimental validation using a classical testing framework shows that the proposed unified approach has improved convergence properties compared to each individual class. These are also demonstrated in a visual tracking application. As a final contribution, our ready-to-use implementation of the algorithm is made publicly available to the research community.